Moving object detection using modified temporal differencing and local fuzzy thresholding
Moving object detection using modified temporal differencing and local fuzzy thresholding
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DOI：
10.1007/s11227-016-1815-7
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发表时间：
2017-03
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影响因子：
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通讯作者：
N. Paul;A. Singh;A. Midya;P. Roy;D. P. Dogra
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文献类型：
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作者：
N. Paul;A. Singh;A. Midya;P. Roy;D. P. Dogra
大多数现有的视频对象检测方案要么计算量大，要么无法检测不同挑战性情况下的移动对象。在本文中，我们提出了一种鲁棒且计算成本低廉的方案来检测视频中的移动对象。三重方法从使用时间信息计算差异图像开始。通过在每个像素位置减去两个输入帧来计算差异图像。我们建议使用以当前帧为中心的固定数量的交替帧，而不是使用传统的连续帧差异方法生成差异图像。这种方法有助于降低计算复杂性，而不影响差异图像的质量。在计算差异图像后，采用伽马校正因子和马哈拉诺比斯距离度量来减少误报和漏报，采用新颖的后处理方案。最后通过局部模糊阈值方案对细化的差异图像进行对象分割。这避免了硬阈值处理中通常遇到的问题，尤其是像素错误分类，这是最重要的一个。为了进行稳健的实验分析，使用了changedection.net、CAVIAR 和 http://perception.i2r 数据集的视频。这些选定的视频包含对象检测过程中面临的各种常见挑战。一些例子是动态背景、阴影、恶劣天气等的存在。结果在定性和定量上证实了所提出的方案相对于一些现有方案的有效性，如实验结果部分所述。
Most of the existing video object detection schemes are either computationally extensive or fail to detect moving objects in different challenging situations. In this paper, we propose a robust and computationally inexpensive scheme to detect moving objects in video. The threefold approach begins with computation of difference images using temporal information. Difference images are calculated by subtracting two input frames, at each pixel position. Instead of generating difference images using the traditional continuous frame difference approach, we propose using a fixed number of alternate frames centered around the current frame. This approach aids in reducing the computational complexity without compromising on quality of the difference images. After computation of difference images, a novel post-processing scheme is employed by utilizing gamma correction factor and Mahalanobis distance metric to reduce false positives and false negatives. Object segmentation is finally performed on the refined difference image by a local fuzzy thresholding scheme. This avoids problems that are usually encountered in hard thresholding, especially pixel misclassification, which is the most important one. For robust experimental analysis, videos from changedetction.net, CAVIAR, and http://perception.i2r datasets have been used. These selected videos contain a wide variety of common challenges faced during object detection. Some examples are the presence of dynamic backgrounds, shadows, bad weather, etc. The results establish the effectiveness of the proposed scheme over some of the existing schemes both qualitatively and quantitatively as delineated in the experimental result section.